Estimation and model performance of aerosol radiative forcing from Radiative Transfer models using the AERONET data over Kenya, East Africa

The rapid economic growth and a lack of strict implementation of air quality regulations have led to unqualified effects on the health, the environment, and the climate. In recent years, there has been an urgent need for the increased characterization of aerosols in terms of optical, micro-physical, and radiative properties and their climatic impacts. In the present study, the aerosol-climate implications were evaluated by aerosol radiative forcing (ARF) at three environmentally specific sites, Malindi (3 degrees S, 40.2 degrees E, 12 m asl), Mbita (0.42 degrees S, 34.20 degrees E, 1125 m asl), and Nairobi (1.0 degrees S, 36.0 degrees E, 1650 m asl), located in Kenya. The aerosol optical properties such as aerosol optical depth (AOD(440)), single scattering albedo (SSA(440)), asymmetric parameter (ASY(440)), and & Aring;ngstr & ouml;m exponent (AE(440-870)) showed significant annual and seasonal heterogeneities. They noticed high (low) values during the local dry (wet) seasons, attributed to changes in anthropogenic activities, emission sources, and prevailing dynamics played by the meteorology. The study also utilized measured spectral aerosol optical properties and two radiative transfer models in the short-wave (SW) and long-wave (LW) regions with their respective spectral bands (0.2-4.0 mu m) and (4.0-100.0 mu m) during 2007-2018. The direct ARF simulated in the SW using the Santa Barbara DISORT Atmospheric Radiative Transfer Model (SBDART) and the Coupled Ocean and Atmospheric Radiative Transfer models (COART) depicted high correspondence, evidenced by relatively high (r > 0.64) correlations over the three sites. The ARF simulated by the two models in the SW and LW regions exhibited significant seasonal heterogeneity, with maximum (minimum) values observed during the local dry (wet) seasons. The results from the present study could form a basis for other climate change studies and increase the accuracy of the existing climate models to enhance climate forecasting over the East Africa region.